# !pip install git+https://github.com/alberanid/imdbpy
# !pip install pandas
# !pip install numpy
# !pip install matplotlib
# !pip install seaborn
# !pip install pandas_profiling --upgrade
# !pip install plotly
# !pip install wordcloud
# !pip install Flask

# Import Dataset
# Import File from Loacal Drive
# from google.colab import files
# data_to_load = files.upload()
# from google.colab import drive
# drive.mount('/content/drive')

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import collections
import plotly.express as px
import plotly.graph_objects as go
import nltk
import re
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.probability import FreqDist
from nltk.util import ngrams
from plotly.subplots import make_subplots
from plotly.offline import iplot, init_notebook_mode
from wordcloud import WordCloud, STOPWORDS
from pandas_profiling import ProfileReport
%matplotlib inline
warnings.filterwarnings("ignore")

nltk.download('all')

[nltk_data] Downloading collection 'all'
[nltk_data]    | 
[nltk_data]    | Downloading package abc to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package abc is already up-to-date!
[nltk_data]    | Downloading package alpino to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package alpino is already up-to-date!
[nltk_data]    | Downloading package biocreative_ppi to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package biocreative_ppi is already up-to-date!
[nltk_data]    | Downloading package brown to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package brown is already up-to-date!
[nltk_data]    | Downloading package brown_tei to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package brown_tei is already up-to-date!
[nltk_data]    | Downloading package cess_cat to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package cess_cat is already up-to-date!
[nltk_data]    | Downloading package cess_esp to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package cess_esp is already up-to-date!
[nltk_data]    | Downloading package chat80 to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package chat80 is already up-to-date!
[nltk_data]    | Downloading package city_database to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package city_database is already up-to-date!
[nltk_data]    | Downloading package cmudict to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package cmudict is already up-to-date!
[nltk_data]    | Downloading package comparative_sentences to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package comparative_sentences is already up-to-
[nltk_data]    |       date!
[nltk_data]    | Downloading package comtrans to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package comtrans is already up-to-date!
[nltk_data]    | Downloading package conll2000 to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package conll2000 is already up-to-date!
[nltk_data]    | Downloading package conll2002 to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package conll2002 is already up-to-date!
[nltk_data]    | Downloading package conll2007 to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package conll2007 is already up-to-date!
[nltk_data]    | Downloading package crubadan to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package crubadan is already up-to-date!
[nltk_data]    | Downloading package dependency_treebank to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package dependency_treebank is already up-to-date!
[nltk_data]    | Downloading package dolch to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package dolch is already up-to-date!
[nltk_data]    | Downloading package europarl_raw to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package europarl_raw is already up-to-date!
[nltk_data]    | Downloading package floresta to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package floresta is already up-to-date!
[nltk_data]    | Downloading package framenet_v15 to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package framenet_v15 is already up-to-date!
[nltk_data]    | Downloading package framenet_v17 to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package framenet_v17 is already up-to-date!
[nltk_data]    | Downloading package gazetteers to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package gazetteers is already up-to-date!
[nltk_data]    | Downloading package genesis to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package genesis is already up-to-date!
[nltk_data]    | Downloading package gutenberg to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package gutenberg is already up-to-date!
[nltk_data]    | Downloading package ieer to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package ieer is already up-to-date!
[nltk_data]    | Downloading package inaugural to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package inaugural is already up-to-date!
[nltk_data]    | Downloading package indian to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package indian is already up-to-date!
[nltk_data]    | Downloading package jeita to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package jeita is already up-to-date!
[nltk_data]    | Downloading package kimmo to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package kimmo is already up-to-date!
[nltk_data]    | Downloading package knbc to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package knbc is already up-to-date!
[nltk_data]    | Downloading package lin_thesaurus to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package lin_thesaurus is already up-to-date!
[nltk_data]    | Downloading package mac_morpho to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package mac_morpho is already up-to-date!
[nltk_data]    | Downloading package machado to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package machado is already up-to-date!
[nltk_data]    | Downloading package masc_tagged to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package masc_tagged is already up-to-date!
[nltk_data]    | Downloading package moses_sample to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package moses_sample is already up-to-date!
[nltk_data]    | Downloading package movie_reviews to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package movie_reviews is already up-to-date!
[nltk_data]    | Downloading package names to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package names is already up-to-date!
[nltk_data]    | Downloading package nombank.1.0 to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package nombank.1.0 is already up-to-date!
[nltk_data]    | Downloading package nps_chat to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package nps_chat is already up-to-date!
[nltk_data]    | Downloading package omw to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package omw is already up-to-date!
[nltk_data]    | Downloading package opinion_lexicon to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package opinion_lexicon is already up-to-date!
[nltk_data]    | Downloading package paradigms to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package paradigms is already up-to-date!
[nltk_data]    | Downloading package pil to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package pil is already up-to-date!
[nltk_data]    | Downloading package pl196x to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package pl196x is already up-to-date!
[nltk_data]    | Downloading package ppattach to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package ppattach is already up-to-date!
[nltk_data]    | Downloading package problem_reports to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package problem_reports is already up-to-date!
[nltk_data]    | Downloading package propbank to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package propbank is already up-to-date!
[nltk_data]    | Downloading package ptb to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package ptb is already up-to-date!
[nltk_data]    | Downloading package product_reviews_1 to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package product_reviews_1 is already up-to-date!
[nltk_data]    | Downloading package product_reviews_2 to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package product_reviews_2 is already up-to-date!
[nltk_data]    | Downloading package pros_cons to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package pros_cons is already up-to-date!
[nltk_data]    | Downloading package qc to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package qc is already up-to-date!
[nltk_data]    | Downloading package reuters to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package reuters is already up-to-date!
[nltk_data]    | Downloading package rte to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package rte is already up-to-date!
[nltk_data]    | Downloading package semcor to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package semcor is already up-to-date!
[nltk_data]    | Downloading package senseval to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package senseval is already up-to-date!
[nltk_data]    | Downloading package sentiwordnet to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package sentiwordnet is already up-to-date!
[nltk_data]    | Downloading package sentence_polarity to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package sentence_polarity is already up-to-date!
[nltk_data]    | Downloading package shakespeare to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package shakespeare is already up-to-date!
[nltk_data]    | Downloading package sinica_treebank to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package sinica_treebank is already up-to-date!
[nltk_data]    | Downloading package smultron to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package smultron is already up-to-date!
[nltk_data]    | Downloading package state_union to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package state_union is already up-to-date!
[nltk_data]    | Downloading package stopwords to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package stopwords is already up-to-date!
[nltk_data]    | Downloading package subjectivity to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package subjectivity is already up-to-date!
[nltk_data]    | Downloading package swadesh to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package swadesh is already up-to-date!
[nltk_data]    | Downloading package switchboard to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package switchboard is already up-to-date!
[nltk_data]    | Downloading package timit to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package timit is already up-to-date!
[nltk_data]    | Downloading package toolbox to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package toolbox is already up-to-date!
[nltk_data]    | Downloading package treebank to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package treebank is already up-to-date!
[nltk_data]    | Downloading package twitter_samples to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package twitter_samples is already up-to-date!
[nltk_data]    | Downloading package udhr to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package udhr is already up-to-date!
[nltk_data]    | Downloading package udhr2 to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package udhr2 is already up-to-date!
[nltk_data]    | Downloading package unicode_samples to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package unicode_samples is already up-to-date!
[nltk_data]    | Downloading package universal_treebanks_v20 to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package universal_treebanks_v20 is already up-to-
[nltk_data]    |       date!
[nltk_data]    | Downloading package verbnet to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package verbnet is already up-to-date!
[nltk_data]    | Downloading package verbnet3 to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package verbnet3 is already up-to-date!
[nltk_data]    | Downloading package webtext to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package webtext is already up-to-date!
[nltk_data]    | Downloading package wordnet to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package wordnet is already up-to-date!
[nltk_data]    | Downloading package wordnet_ic to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package wordnet_ic is already up-to-date!
[nltk_data]    | Downloading package words to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package words is already up-to-date!
[nltk_data]    | Downloading package ycoe to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package ycoe is already up-to-date!
[nltk_data]    | Downloading package rslp to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package rslp is already up-to-date!
[nltk_data]    | Downloading package maxent_treebank_pos_tagger to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package maxent_treebank_pos_tagger is already up-
[nltk_data]    |       to-date!
[nltk_data]    | Downloading package universal_tagset to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package universal_tagset is already up-to-date!
[nltk_data]    | Downloading package maxent_ne_chunker to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package maxent_ne_chunker is already up-to-date!
[nltk_data]    | Downloading package punkt to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package punkt is already up-to-date!
[nltk_data]    | Downloading package book_grammars to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package book_grammars is already up-to-date!
[nltk_data]    | Downloading package sample_grammars to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package sample_grammars is already up-to-date!
[nltk_data]    | Downloading package spanish_grammars to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package spanish_grammars is already up-to-date!
[nltk_data]    | Downloading package basque_grammars to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package basque_grammars is already up-to-date!
[nltk_data]    | Downloading package large_grammars to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package large_grammars is already up-to-date!
[nltk_data]    | Downloading package tagsets to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package tagsets is already up-to-date!
[nltk_data]    | Downloading package snowball_data to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package snowball_data is already up-to-date!
[nltk_data]    | Downloading package bllip_wsj_no_aux to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package bllip_wsj_no_aux is already up-to-date!
[nltk_data]    | Downloading package word2vec_sample to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package word2vec_sample is already up-to-date!
[nltk_data]    | Downloading package panlex_swadesh to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package panlex_swadesh is already up-to-date!
[nltk_data]    | Downloading package mte_teip5 to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package mte_teip5 is already up-to-date!
[nltk_data]    | Downloading package averaged_perceptron_tagger to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package averaged_perceptron_tagger is already up-
[nltk_data]    |       to-date!
[nltk_data]    | Downloading package averaged_perceptron_tagger_ru to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package averaged_perceptron_tagger_ru is already
[nltk_data]    |       up-to-date!
[nltk_data]    | Downloading package perluniprops to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package perluniprops is already up-to-date!
[nltk_data]    | Downloading package nonbreaking_prefixes to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package nonbreaking_prefixes is already up-to-date!
[nltk_data]    | Downloading package vader_lexicon to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package vader_lexicon is already up-to-date!
[nltk_data]    | Downloading package porter_test to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package porter_test is already up-to-date!
[nltk_data]    | Downloading package wmt15_eval to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package wmt15_eval is already up-to-date!
[nltk_data]    | Downloading package mwa_ppdb to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package mwa_ppdb is already up-to-date!
[nltk_data]    | 
[nltk_data]  Done downloading collection all

True

# path = '/content/drive/MyDrive/Files/'

path = 'C:\\Users\\pawan\\OneDrive\\Desktop\\ott\\Data\\'
 
df_tvshows = pd.read_csv(path + 'otttvshows.csv')
 
df_tvshows.head()

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	Language	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type
0	1	Snowpiercer	2013	18+	6.9	94%	NaN	Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso...	Action,Drama,Sci-Fi,Thriller	United States	English	Set seven years after the world has become a f...	60.0	tv series	3.0	1	0	0	0	1
1	2	Philadelphia	1993	13+	8.8	80%	NaN	Charlie Day,Glenn Howerton,Rob McElhenney,Kait...	Comedy	United States	English	The gang, 5 raging alcoholic, narcissists run ...	22.0	tv series	18.0	1	0	0	0	1
2	3	Roma	2018	18+	8.7	93%	NaN	Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ...	Action,Drama,History,Romance,War	United Kingdom,United States	English	In this British historical drama, the turbulen...	52.0	tv series	2.0	1	0	0	0	1
3	4	Amy	2015	18+	7.0	87%	NaN	Amy Brenneman,Richard T. Jones,Jessica Tuck,Ma...	Drama	United States	English	A family drama focused on three generations of...	60.0	tv series	6.0	1	0	1	1	1
4	5	The Young Offenders	2016	NaN	8.0	100%	NaN	Alex Murphy,Chris Walley,Hilary Rose,Dominic M...	Comedy	United Kingdom,Ireland	English	NaN	30.0	tv series	3.0	1	0	0	0	1

# profile = ProfileReport(df_tvshows)
# profile

def data_investigate(df):
    print('No of Rows : ', df.shape[0])
    print('No of Coloums : ', df.shape[1])
    print('**'*25)
    print('Colums Names : \n', df.columns)
    print('**'*25)
    print('Datatype of Columns : \n', df.dtypes)
    print('**'*25)
    print('Missing Values : ')
    c = df.isnull().sum()
    c = c[c > 0]
    print(c)
    print('**'*25)
    print('Missing vaules %age wise :\n')
    print((100*(df.isnull().sum()/len(df.index))))
    print('**'*25)
    print('Pictorial Representation : ')
    plt.figure(figsize = (10, 10))
    sns.heatmap(df.isnull(), yticklabels = False, cbar = False)
    plt.show()

data_investigate(df_tvshows)

No of Rows :  5432
No of Coloums :  20
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb               float64
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime            float64
Kind                object
Seasons            float64
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
dtype: object
**************************************************
Missing Values : 
Age                1954
IMDb                556
Rotten Tomatoes    4194
Directors          5158
Cast                486
Genres              323
Country             549
Language            638
Plotline           2493
Runtime            1410
Seasons             679
dtype: int64
**************************************************
Missing vaules %age wise :

ID                  0.000000
Title               0.000000
Year                0.000000
Age                35.972018
IMDb               10.235641
Rotten Tomatoes    77.209131
Directors          94.955817
Cast                8.946981
Genres              5.946244
Country            10.106775
Language           11.745214
Plotline           45.894698
Runtime            25.957290
Kind                0.000000
Seasons            12.500000
Netflix             0.000000
Hulu                0.000000
Prime Video         0.000000
Disney+             0.000000
Type                0.000000
dtype: float64
**************************************************
Pictorial Representation : 

# ID
# df_tvshows = df_tvshows.drop(['ID'], axis = 1)
 
# Age
df_tvshows.loc[df_tvshows['Age'].isnull() & df_tvshows['Disney+'] == 1, "Age"] = '13'
# df_tvshows.fillna({'Age' : 18}, inplace = True)
df_tvshows.fillna({'Age' : 'NR'}, inplace = True)
df_tvshows['Age'].replace({'all': '0'}, inplace = True)
df_tvshows['Age'].replace({'7+': '7'}, inplace = True)
df_tvshows['Age'].replace({'13+': '13'}, inplace = True)
df_tvshows['Age'].replace({'16+': '16'}, inplace = True)
df_tvshows['Age'].replace({'18+': '18'}, inplace = True)
# df_tvshows['Age'] = df_tvshows['Age'].astype(int)
 
# IMDb
# df_tvshows.fillna({'IMDb' : df_tvshows['IMDb'].mean()}, inplace = True)
# df_tvshows.fillna({'IMDb' : df_tvshows['IMDb'].median()}, inplace = True)
df_tvshows.fillna({'IMDb' : "NA"}, inplace = True)
 
# Rotten Tomatoes
df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'][df_tvshows['Rotten Tomatoes'].notnull()].str.replace('%', '').astype(int)
# df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'][df_tvshows['Rotten Tomatoes'].notnull()].astype(int)
# df_tvshows.fillna({'Rotten Tomatoes' : df_tvshows['Rotten Tomatoes'].mean()}, inplace = True)
# df_tvshows.fillna({'Rotten Tomatoes' : df_tvshows['Rotten Tomatoes'].median()}, inplace = True)
# df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'].astype(int)
df_tvshows.fillna({'Rotten Tomatoes' : "NA"}, inplace = True)
 
# Directors
# df_tvshows = df_tvshows.drop(['Directors'], axis = 1)
df_tvshows.fillna({'Directors' : "NA"}, inplace = True)
 
# Cast
df_tvshows.fillna({'Cast' : "NA"}, inplace = True)
 
# Genres
df_tvshows.fillna({'Genres': "NA"}, inplace = True)
 
# Country
df_tvshows.fillna({'Country': "NA"}, inplace = True)
 
# Language
df_tvshows.fillna({'Language': "NA"}, inplace = True)
 
# Plotline
df_tvshows.fillna({'Plotline': "NA"}, inplace = True)
 
# Runtime
# df_tvshows.fillna({'Runtime' : df_tvshows['Runtime'].mean()}, inplace = True)
# df_tvshows['Runtime'] = df_tvshows['Runtime'].astype(int)
df_tvshows.fillna({'Runtime' : "NA"}, inplace = True)
 
# Kind
# df_tvshows.fillna({'Kind': "NA"}, inplace = True)
 
# Type
# df_tvshows.fillna({'Type': "NA"}, inplace = True)
# df_tvshows = df_tvshows.drop(['Type'], axis = 1)
 
# Seasons
# df_tvshows.fillna({'Seasons': 1}, inplace = True)
df_tvshows.fillna({'Seasons': "NA"}, inplace = True)
# df_tvshows = df_tvshows.drop(['Seasons'], axis = 1)
# df_tvshows['Seasons'] = df_tvshows['Seasons'].astype(int)
# df_tvshows.fillna({'Seasons' : df_tvshows['Seasons'].mean()}, inplace = True)
# df_tvshows['Seasons'] = df_tvshows['Seasons'].astype(int)
 
# Service Provider
df_tvshows['Service Provider'] = df_tvshows.loc[:, ['Netflix', 'Prime Video', 'Disney+', 'Hulu']].idxmax(axis = 1)
# df_tvshows.drop(['Netflix','Prime Video','Disney+','Hulu'], axis = 1)

# Removing Duplicate and Missing Entries
df_tvshows.dropna(how = 'any', inplace = True)
df_tvshows.drop_duplicates(inplace = True)

data_investigate(df_tvshows)

No of Rows :  5432
No of Coloums :  21
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type',
       'Service Provider'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb                object
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime             object
Kind                object
Seasons             object
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
Service Provider    object
dtype: object
**************************************************
Missing Values : 
Series([], dtype: int64)
**************************************************
Missing vaules %age wise :

ID                  0.0
Title               0.0
Year                0.0
Age                 0.0
IMDb                0.0
Rotten Tomatoes     0.0
Directors           0.0
Cast                0.0
Genres              0.0
Country             0.0
Language            0.0
Plotline            0.0
Runtime             0.0
Kind                0.0
Seasons             0.0
Netflix             0.0
Hulu                0.0
Prime Video         0.0
Disney+             0.0
Type                0.0
Service Provider    0.0
dtype: float64
**************************************************
Pictorial Representation : 

df_tvshows.head()

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
0	1	Snowpiercer	2013	18	6.9	94	NA	Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso...	Action,Drama,Sci-Fi,Thriller	United States	...	Set seven years after the world has become a f...	60	tv series	3	1	0	0	0	1	Netflix
1	2	Philadelphia	1993	13	8.8	80	NA	Charlie Day,Glenn Howerton,Rob McElhenney,Kait...	Comedy	United States	...	The gang, 5 raging alcoholic, narcissists run ...	22	tv series	18	1	0	0	0	1	Netflix
2	3	Roma	2018	18	8.7	93	NA	Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ...	Action,Drama,History,Romance,War	United Kingdom,United States	...	In this British historical drama, the turbulen...	52	tv series	2	1	0	0	0	1	Netflix
3	4	Amy	2015	18	7	87	NA	Amy Brenneman,Richard T. Jones,Jessica Tuck,Ma...	Drama	United States	...	A family drama focused on three generations of...	60	tv series	6	1	0	1	1	1	Netflix
4	5	The Young Offenders	2016	NR	8	100	NA	Alex Murphy,Chris Walley,Hilary Rose,Dominic M...	Comedy	United Kingdom,Ireland	...	NA	30	tv series	3	1	0	0	0	1	Netflix

5 rows × 21 columns

df_tvshows.describe()

	ID	Year	Netflix	Hulu	Prime Video	Disney+	Type
count	5432.000000	5432.000000	5432.000000	5432.000000	5432.000000	5432.000000	5432.0
mean	2716.500000	2010.668446	0.341311	0.293999	0.403351	0.033689	1.0
std	1568.227662	11.726176	0.474193	0.455633	0.490615	0.180445	0.0
min	1.000000	1901.000000	0.000000	0.000000	0.000000	0.000000	1.0
25%	1358.750000	2009.000000	0.000000	0.000000	0.000000	0.000000	1.0
50%	2716.500000	2014.000000	0.000000	0.000000	0.000000	0.000000	1.0
75%	4074.250000	2017.000000	1.000000	1.000000	1.000000	0.000000	1.0
max	5432.000000	2020.000000	1.000000	1.000000	1.000000	1.000000	1.0

df_tvshows.corr()

	ID	Year	Netflix	Hulu	Prime Video	Disney+	Type
ID	1.000000	-0.031346	-0.646330	0.034293	0.441264	0.195409	NaN
Year	-0.031346	1.000000	0.222316	-0.065807	-0.198675	-0.022741	NaN
Netflix	-0.646330	0.222316	1.000000	-0.366515	-0.515086	-0.119344	NaN
Hulu	0.034293	-0.065807	-0.366515	1.000000	-0.377374	-0.075701	NaN
Prime Video	0.441264	-0.198675	-0.515086	-0.377374	1.000000	-0.151442	NaN
Disney+	0.195409	-0.022741	-0.119344	-0.075701	-0.151442	1.000000	NaN
Type	NaN	NaN	NaN	NaN	NaN	NaN	NaN

# df_tvshows.sort_values('Year', ascending = True)
# df_tvshows.sort_values('IMDb', ascending = False)

# df_tvshows.to_csv(path_or_buf= '/content/drive/MyDrive/Files/updated_otttvshows.csv', index = False)
 
# path = '/content/drive/MyDrive/Files/'
 
# udf_tvshows = pd.read_csv(path + 'updated_otttvshows.csv')
 
# udf_tvshows

# df_netflix_tvshows = df_tvshows.loc[(df_tvshows['Netflix'] > 0)]
# df_hulu_tvshows = df_tvshows.loc[(df_tvshows['Hulu'] > 0)]
# df_prime_video_tvshows = df_tvshows.loc[(df_tvshows['Prime Video'] > 0)]
# df_disney_tvshows = df_tvshows.loc[(df_tvshows['Disney+'] > 0)]

df_netflix_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 1) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 0)]
df_hulu_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 1) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 0)]
df_prime_video_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 1 ) & (df_tvshows['Disney+'] == 0)]
df_disney_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 1)]

df_tvshows_years = df_tvshows.copy()

df_tvshows_years.drop(df_tvshows_years.loc[df_tvshows_years['Year'] == "NA"].index, inplace = True)
# df_tvshows_years = df_tvshows_years[df_tvshows_years.Year != "NA"]
df_tvshows_years['Year'] = df_tvshows_years['Year'].astype(int)

# Creating distinct dataframes only with the tvshows present on individual streaming platforms
netflix_years_tvshows = df_tvshows_years.loc[df_tvshows_years['Netflix'] == 1]
hulu_years_tvshows = df_tvshows_years.loc[df_tvshows_years['Hulu'] == 1]
prime_video_years_tvshows = df_tvshows_years.loc[df_tvshows_years['Prime Video'] == 1]
disney_years_tvshows = df_tvshows_years.loc[df_tvshows_years['Disney+'] == 1]

df_tvshows_years_group = df_tvshows_years.copy()

plt.figure(figsize = (10, 10))
corr = df_tvshows_years.corr()
# Plot figsize
fig, ax = plt.subplots(figsize=(10, 8))
# Generate Heat Map, allow annotations and place floats in map
sns.heatmap(corr, cmap = 'magma', annot = True, fmt = ".2f")
# Apply xticks
plt.xticks(range(len(corr.columns)), corr.columns);
# Apply yticks
plt.yticks(range(len(corr.columns)), corr.columns)
# show plot
plt.show()
fig.show()

<Figure size 720x720 with 0 Axes>

df_years_high_tvshows = df_tvshows_years.sort_values(by = 'Year', ascending = False).reset_index()
df_years_high_tvshows = df_years_high_tvshows.drop(['index'], axis = 1)
# filter = (df_tvshows_years['Year'] == (df_tvshows_years['Year'].max()))
# df_years_high_tvshows = df_tvshows_years[filter]
 
# highest_rated_tvshows = df_tvshows_years.loc[df_tvshows_years['Year'].idxmax()]
 
print('\nTV Shows with Highest Ever Year  are : \n')
df_years_high_tvshows.head(5)

TV Shows with Highest Ever Year  are : 

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
0	894	Gentefied	2020	18	7.4	91	NA	Joaquín Cosio,Joseph Julian Soria,Karrie Marti...	Comedy	United States	...	NA	NA	tv series	2	1	0	0	0	1	Netflix
1	3004	BOFURI: I Don’t Want to Get Hurt, so I’ll Max ...	2020	16	7.5	NA	NA	Jad Saxton,Megan Shipman,Anthony Bowling,Tia L...	Animation,Action,Adventure,Comedy,Fantasy,Sci-Fi	Japan	...	NA	23	tv series	1	0	1	0	0	1	Hulu
2	940	The Pharmacist	2020	18	7.7	89	NA	NA	Documentary,Crime	United States	...	Seventeen year-old Kim is the pride and joy of...	217	tv series	1	1	0	0	0	1	Netflix
3	3038	The Bachelor Presents: Listen to Your Heart	2020	16	4.9	NA	NA	Chris Harrison,Jamie Gabrielle,Matt Ranaudo,Br...	Drama,Game-Show,Music,Reality-TV,Romance	United States	...	NA	120	tv series	1	0	1	0	0	1	Hulu
4	1425	Almost Happy	2020	NR	6.8	NA	NA	Sebastián Wainraich,Natalie Pérez,Santiago Kor...	Comedy	Argentina	...	NA	NA	tv series	1	1	0	0	0	1	Netflix

5 rows × 21 columns

fig = px.bar(y = df_years_high_tvshows['Title'][:15],
             x = df_years_high_tvshows['Year'][:15], 
             color = df_years_high_tvshows['Year'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Year : In Minutes'},
             title  = 'TV Shows with Highest Year in Minutes : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

df_years_low_tvshows = df_tvshows_years.sort_values(by = 'Year', ascending = True).reset_index()
df_years_low_tvshows = df_years_low_tvshows.drop(['index'], axis = 1)
# filter = (df_tvshows_years['Year'] == (df_tvshows_years['Year'].min()))
# df_years_low_tvshows = df_tvshows_years[filter]

print('\nTV Shows with Lowest Ever Year  are : \n')
df_years_low_tvshows.head(5)

TV Shows with Lowest Ever Year  are : 

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
0	4665	Gods & Monsters with Tony Robinson	1901	NR	7.3	NA	NA	Tony Robinson,Little Woodham Villagers,Peter M...	History	United Kingdom	...	NA	60	tv series	1	0	0	1	0	1	Prime Video
1	4684	History of Westinghouse	1904	NR	NA	NA	NA	NA	NA	NA	...	NA	NA	tv series	NA	0	0	1	0	1	Prime Video
2	2204	Born To Explore	1914	0	7.5	NA	NA	Richard Wiese,Kenneth Lacovara,Belle Aykroyd,D...	Adventure	United States	...	NA	30	tv series	8	1	0	0	0	1	Netflix
3	126	Nosferatu	1922	NR	6.7	97	NA	Ashleigh Cummings,Ólafur Darri Ólafsson,Jahkar...	Drama,Fantasy,Horror,Mystery	United States	...	A young Victoria "Vic" McQueen discovers she h...	60	tv series	2	0	0	1	0	1	Prime Video
4	139	College	1927	NR	6.8	12	NA	Tom Hanks,Peter Scolari,Donna Dixon,Holland Ta...	Comedy	United States	...	After her husband's death, Hana lives on alone...	30	tv series	2	0	0	1	0	1	Prime Video

5 rows × 21 columns

fig = px.bar(y = df_years_low_tvshows['Title'][:15],
             x = df_years_low_tvshows['Year'][:15], 
             color = df_years_low_tvshows['Year'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Year : In Minutes'},
             title  = 'TV Shows with Lowest Year in Minutes : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

print(f'''
      Total '{df_tvshows_years['Year'].unique().shape[0]}' unique Year s were Given, They were Like this,\n
      
{df_tvshows_years.sort_values(by = 'Year', ascending = False)['Year'].unique()}\n
 
      The Highest Ever Year Ever Any TV Show Got is '{df_years_high_tvshows['Title'][0]}' : '{df_years_high_tvshows['Year'].max()}'\n
 
      The Lowest Ever Year Ever Any TV Show Got is '{df_years_low_tvshows['Title'][0]}' : '{df_years_low_tvshows['Year'].min()}'\n
      ''')

      Total '89' unique Year s were Given, They were Like this,

      
[2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007
 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993
 1992 1991 1990 1989 1988 1987 1986 1985 1984 1983 1982 1981 1980 1979
 1978 1977 1976 1975 1974 1973 1972 1971 1970 1969 1968 1967 1966 1965
 1964 1963 1962 1961 1960 1959 1958 1957 1956 1955 1954 1953 1952 1951
 1950 1949 1948 1947 1946 1945 1944 1943 1942 1940 1938 1937 1936 1932
 1927 1922 1914 1904 1901]

 
      The Highest Ever Year Ever Any TV Show Got is 'Gentefied' : '2020'

 
      The Lowest Ever Year Ever Any TV Show Got is 'Gods & Monsters with Tony Robinson' : '1901'

      

netflix_years_high_tvshows = df_years_high_tvshows.loc[df_years_high_tvshows['Netflix']==1].reset_index()
netflix_years_high_tvshows = netflix_years_high_tvshows.drop(['index'], axis = 1)
 
netflix_years_low_tvshows = df_years_low_tvshows.loc[df_years_low_tvshows['Netflix']==1].reset_index()
netflix_years_low_tvshows = netflix_years_low_tvshows.drop(['index'], axis = 1)
 
netflix_years_high_tvshows.head(5)

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
0	894	Gentefied	2020	18	7.4	91	NA	Joaquín Cosio,Joseph Julian Soria,Karrie Marti...	Comedy	United States	...	NA	NA	tv series	2	1	0	0	0	1	Netflix
1	940	The Pharmacist	2020	18	7.7	89	NA	NA	Documentary,Crime	United States	...	Seventeen year-old Kim is the pride and joy of...	217	tv series	1	1	0	0	0	1	Netflix
2	1425	Almost Happy	2020	NR	6.8	NA	NA	Sebastián Wainraich,Natalie Pérez,Santiago Kor...	Comedy	Argentina	...	NA	NA	tv series	1	1	0	0	0	1	Netflix
3	927	#blackAF	2020	18	6.8	46	NA	Rashida Jones,Kenya Barris,Iman Benson,Genneya...	Comedy	United States	...	Jim Lake Jr. is an ordinary kid with a busy Mo...	36	tv series	2	1	0	0	0	1	Netflix
4	1826	Ainori Love Wagon: African Journey	2020	NR	7.2	NA	NA	Becky,Ryô Katô,Karina Maruyama	Reality-TV	Japan	...	NA	NA	tv series	NA	1	0	0	0	1	Netflix

5 rows × 21 columns

fig = px.bar(y = netflix_years_high_tvshows['Title'][:15],
             x = netflix_years_high_tvshows['Year'][:15], 
             color = netflix_years_high_tvshows['Year'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Year : In Minutes'},
             title  = 'TV Shows with Highest Year in Minutes : Netflix')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

fig = px.bar(y = netflix_years_low_tvshows['Title'][:15],
             x = netflix_years_low_tvshows['Year'][:15], 
             color = netflix_years_low_tvshows['Year'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Year : In Minutes'},
             title  = 'TV Shows with Lowest Year in Minutes : Netflix')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

hulu_years_high_tvshows = df_years_high_tvshows.loc[df_years_high_tvshows['Hulu']==1].reset_index()
hulu_years_high_tvshows = hulu_years_high_tvshows.drop(['index'], axis = 1)
 
hulu_years_low_tvshows = df_years_low_tvshows.loc[df_years_low_tvshows['Hulu']==1].reset_index()
hulu_years_low_tvshows = hulu_years_low_tvshows.drop(['index'], axis = 1)
 
hulu_years_high_tvshows.head(5)

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
0	3004	BOFURI: I Don’t Want to Get Hurt, so I’ll Max ...	2020	16	7.5	NA	NA	Jad Saxton,Megan Shipman,Anthony Bowling,Tia L...	Animation,Action,Adventure,Comedy,Fantasy,Sci-Fi	Japan	...	NA	23	tv series	1	0	1	0	0	1	Hulu
1	3038	The Bachelor Presents: Listen to Your Heart	2020	16	4.9	NA	NA	Chris Harrison,Jamie Gabrielle,Matt Ranaudo,Br...	Drama,Game-Show,Music,Reality-TV,Romance	United States	...	NA	120	tv series	1	0	1	0	0	1	Hulu
2	3015	ID: INVADED	2020	18	7.6	NA	NA	Kenjirô Tsuda,Sarah Emi Bridcutt,Yoshimasa Hos...	Animation,Crime,Drama,Mystery,Sci-Fi,Thriller	Japan	...	We follow a band of American soldiers as they ...	24	tv series	1	0	1	0	0	1	Hulu
3	2999	Council of Dads	2020	16	6.7	50	NA	Sarah Wayne Callies,Clive Standen,J. August Ri...	Drama	United States	...	NA	44	tv series	1	0	1	0	0	1	Hulu
4	2994	Toilet-Bound Hanako-kun	2020	16	7.4	NA	NA	Justin Briner,Megumi Ogata,Tyson Rinehart,Tia ...	Animation,Comedy,Fantasy	Japan	...	An Americanized version of the original Japane...	24	tv series	1	0	1	0	0	1	Hulu

5 rows × 21 columns

fig = px.bar(y = hulu_years_high_tvshows['Title'][:15],
             x = hulu_years_high_tvshows['Year'][:15], 
             color = hulu_years_high_tvshows['Year'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Year : In Minutes'},
             title  = 'TV Shows with Highest Year in Minutes : Hulu')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

fig = px.bar(y = hulu_years_low_tvshows['Title'][:15],
             x = hulu_years_low_tvshows['Year'][:15], 
             color = hulu_years_low_tvshows['Year'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Year : In Minutes'},
             title  = 'TV Shows with Lowest Year in Minutes : Hulu')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

prime_video_years_high_tvshows = df_years_high_tvshows.loc[df_years_high_tvshows['Prime Video']==1].reset_index()
prime_video_years_high_tvshows = prime_video_years_high_tvshows.drop(['index'], axis = 1)
 
prime_video_years_low_tvshows = df_years_low_tvshows.loc[df_years_low_tvshows['Prime Video']==1].reset_index()
prime_video_years_low_tvshows = prime_video_years_low_tvshows.drop(['index'], axis = 1)
 
prime_video_years_high_tvshows.head(5)

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
0	3780	Tales from the Loop	2020	18	7.5	85	NA	Daniel Zolghadri,Paul Schneider,Rebecca Hall,R...	Drama,Sci-Fi	United States	...	God has abandoned Heaven. It's 1985: the Reaga...	50	tv series	1	0	0	1	0	1	Prime Video
1	204	Evolution of Evil	2020	NR	6.5	NA	NA	Alisdair Simpson,Mohamed Atta,Luigi Boccanfuso...	Documentary,Biography,History	Germany,United Kingdom	...	A millionaire is found dead of heart failure h...	50	tv series	1	0	0	1	0	1	Prime Video
2	4461	Jessy & Nessy	2020	0	8	NA	NA	Jamie Buchanan,Alexa Bauer,Shai Matheson,Naomi...	Animation	Ireland	...	Franny's Feet is about a 5 year old girl who v...	NA	tv series	1	0	0	1	0	1	Prime Video
3	3735	Upload	2020	18	8	88	NA	Robbie Amell,Andy Allo,Zainab Johnson,Kevin Bi...	Comedy,Mystery,Sci-Fi	United States	...	When CIA analyst Jack Ryan stumbles upon a sus...	296	tv series	2	0	0	1	0	1	Prime Video
4	3790	Hunters	2020	18	7.2	64	NA	Logan Lerman,Jerrika Hinton,Lena Olin,Saul Rub...	Crime,Drama,Mystery	United States	...	NA	60	tv series	2	0	0	1	0	1	Prime Video

5 rows × 21 columns

fig = px.bar(y = prime_video_years_high_tvshows['Title'][:15],
             x = prime_video_years_high_tvshows['Year'][:15], 
             color = prime_video_years_high_tvshows['Year'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Year : In Minutes'},
             title  = 'TV Shows with Highest Year in Minutes : Prime Video')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

fig = px.bar(y = prime_video_years_low_tvshows['Title'][:15],
             x = prime_video_years_low_tvshows['Year'][:15], 
             color = prime_video_years_low_tvshows['Year'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Year : In Minutes'},
             title  = 'TV Shows with Lowest Year in Minutes : Prime Video')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

disney_years_high_tvshows = df_years_high_tvshows.loc[df_years_high_tvshows['Disney+']==1].reset_index()
disney_years_high_tvshows = disney_years_high_tvshows.drop(['index'], axis = 1)
 
disney_years_low_tvshows = df_years_low_tvshows.loc[df_years_low_tvshows['Disney+']==1].reset_index()
disney_years_low_tvshows = disney_years_low_tvshows.drop(['index'], axis = 1)
 
disney_years_high_tvshows.head(5)

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
0	5395	It's A Dog's Life	2020	0	8.2	NA	NA	Bill Farmer,Esther Abshier,Linda Castaneda,Eba...	Documentary	United States	...	NA	22	tv series	1	0	0	0	1	1	Disney+
1	5342	Diary of a Future President	2020	7	5.9	100	NA	Sanai Victoria,Nathan Arenas,Tess Romero,Selen...	Comedy,Drama,Family	United States	...	Executive producer Kristen Bell, who also appe...	30	tv series	2	0	0	0	1	1	Disney+
2	5333	Prop Culture	2020	7	8.2	NA	NA	Dan Lanigan,Don Bies,Andrew Adamson,Erin Andre...	Documentary	United States	...	Peter Parker has been Spider-Man for eight yea...	35	tv series	1	0	0	0	1	1	Disney+
3	5318	Disney Gallery / Star Wars: The Mandalorian	2020	7	8.5	100	Josiah Swanson	Josiah Swanson	Talk-Show	NA	...	NA	NA	tv series	NA	0	0	0	1	1	Disney+
4	487	Stargirl	2020	7	7.3	70	NA	Brec Bassinger,Yvette Monreal,Anjelika Washing...	Action,Adventure,Crime,Drama,Fantasy,Sci-Fi	United States	...	NA	566	tv series	2	0	0	0	1	1	Disney+

5 rows × 21 columns

fig = px.bar(y = disney_years_high_tvshows['Title'][:15],
             x = disney_years_high_tvshows['Year'][:15], 
             color = disney_years_high_tvshows['Year'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Year : In Minutes'},
             title  = 'TV Shows with Highest Year in Minutes : Disney+')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

fig = px.bar(y = disney_years_low_tvshows['Title'][:15],
             x = disney_years_low_tvshows['Year'][:15], 
             color = disney_years_low_tvshows['Year'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Year : In Minutes'},
             title  = 'TV Shows with Lowest Year in Minutes : Disney+')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

print(f'''
      The TV Show with Highest Year  Ever Got is '{df_years_high_tvshows['Title'][0]}' : '{df_years_high_tvshows['Year'].max()}'\n
      The TV Show with Lowest Year  Ever Got is '{df_years_low_tvshows['Title'][0]}' : '{df_years_low_tvshows['Year'].min()}'\n
      
      The TV Show with Highest Year  on 'Netflix' is '{netflix_years_high_tvshows['Title'][0]}' : '{netflix_years_high_tvshows['Year'].max()}'\n
      The TV Show with Lowest Year  on 'Netflix' is '{netflix_years_low_tvshows['Title'][0]}' : '{netflix_years_low_tvshows['Year'].min()}'\n
      
      The TV Show with Highest Year  on 'Hulu' is '{hulu_years_high_tvshows['Title'][0]}' : '{hulu_years_high_tvshows['Year'].max()}'\n
      The TV Show with Lowest Year  on 'Hulu' is '{hulu_years_low_tvshows['Title'][0]}' : '{hulu_years_low_tvshows['Year'].min()}'\n
      
      The TV Show with Highest Year  on 'Prime Video' is '{prime_video_years_high_tvshows['Title'][0]}' : '{prime_video_years_high_tvshows['Year'].max()}'\n
      The TV Show with Lowest Year  on 'Prime Video' is '{prime_video_years_low_tvshows['Title'][0]}' : '{prime_video_years_low_tvshows['Year'].min()}'\n
      
      The TV Show with Highest Year  on 'Disney+' is '{disney_years_high_tvshows['Title'][0]}' : '{disney_years_high_tvshows['Year'].max()}'\n
      The TV Show with Lowest Year  on 'Disney+' is '{disney_years_low_tvshows['Title'][0]}' : '{disney_years_low_tvshows['Year'].min()}'\n 
      ''')

      The TV Show with Highest Year  Ever Got is 'Gentefied' : '2020'

      The TV Show with Lowest Year  Ever Got is 'Gods & Monsters with Tony Robinson' : '1901'

      
      The TV Show with Highest Year  on 'Netflix' is 'Gentefied' : '2020'

      The TV Show with Lowest Year  on 'Netflix' is 'Born To Explore' : '1914'

      
      The TV Show with Highest Year  on 'Hulu' is 'BOFURI: I Don’t Want to Get Hurt, so I’ll Max Out My Defense.' : '2020'

      The TV Show with Lowest Year  on 'Hulu' is 'You Bet Your Life' : '1947'

      
      The TV Show with Highest Year  on 'Prime Video' is 'Tales from the Loop' : '2020'

      The TV Show with Lowest Year  on 'Prime Video' is 'Gods & Monsters with Tony Robinson' : '1901'

      
      The TV Show with Highest Year  on 'Disney+' is 'It's A Dog's Life' : '2020'

      The TV Show with Lowest Year  on 'Disney+' is 'The Plausible Impossible' : '1956'
 
      

print(f'''
      Accross All Platforms the Average Year  is '{round(df_tvshows_years['Year'].mean(), ndigits = 2)}'\n
      The Average Year  on 'Netflix' is '{round(netflix_years_tvshows['Year'].mean(), ndigits = 2)}'\n
      The Average Year  on 'Hulu' is '{round(hulu_years_tvshows['Year'].mean(), ndigits = 2)}'\n
      The Average Year  on 'Prime Video' is '{round(prime_video_years_tvshows['Year'].mean(), ndigits = 2)}'\n
      The Average Year  on 'Disney+' is '{round(disney_years_tvshows['Year'].mean(), ndigits = 2)}'\n 
      ''')

      Accross All Platforms the Average Year  is '2010.67'

      The Average Year  on 'Netflix' is '2014.29'

      The Average Year  on 'Hulu' is '2009.47'

      The Average Year  on 'Prime Video' is '2007.84'

      The Average Year  on 'Disney+' is '2009.24'
 
      

f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(df_tvshows_years['Year'],bins = 20, kde = True, ax = ax[0])
sns.boxplot(df_tvshows_years['Year'], ax = ax[1])
plt.show()

# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Year s Per Platform')
 
# Plotting the information from each dataset into a histogram
sns.histplot(prime_video_years_tvshows['Year'][:100], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_years_tvshows['Year'][:100], color = 'red', legend = True, kde = True)
sns.histplot(hulu_years_tvshows['Year'][:100], color = 'lightgreen', legend = True, kde = True)
sns.histplot(disney_years_tvshows['Year'][:100], color = 'darkblue', legend = True, kde = True) 
 
# Setting the legend
plt.legend(['Prime Video', 'Netflix', 'Hulu', 'Disney+'])
plt.show()

year_count = df_tvshows_years.groupby('Year')['Title'].count()
year_tvshows = df_tvshows_years.groupby('Year')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
year_data_tvshows = pd.concat([year_count, year_tvshows], axis = 1).reset_index().rename(columns = {'Title' : 'TV Shows Count'})
year_data_tvshows = year_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)

# TV Shows Count per Year - All Platforms Combined
year_data_tvshows.head()

	Year	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
85	2017	605	214	116	276	19
86	2018	554	271	129	162	14
84	2016	543	212	115	226	10
83	2015	429	171	115	165	9
87	2019	382	231	94	44	21

fig = px.bar(y = year_data_tvshows['TV Shows Count'],
             x = year_data_tvshows['Year'], 
             color = year_data_tvshows['Year'],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows Count', 'x' : 'Year : In Minutes'},
             title  = 'TV Shows with Year : All Platforms')

fig.update_layout(plot_bgcolor = "white")
fig.show()

fig = px.pie(year_data_tvshows[:10],
             names = year_data_tvshows['Year'][:10],
             values = year_data_tvshows['TV Shows Count'][:10],
             color = year_data_tvshows['TV Shows Count'][:10],
             color_discrete_sequence = px.colors.sequential.Teal)

fig.update_traces(textinfo = 'percent+label',
                  title = 'TV Shows Count based on Year Group')
fig.show()

# Highest TV Shows Count per Year - All Platforms Combined
df_year_high_tvshows = year_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False).reset_index()
df_year_high_tvshows = df_year_high_tvshows.drop(['index'], axis = 1)
# filter = (year_data_tvshows['TV Shows Count'] = =  (year_data_tvshows['TV Shows Count'].max()))
# df_year_high_tvshows = year_data_tvshows[filter]
 
# highest_rated_tvshows = year_data_tvshows.loc[year_data_tvshows['TV Shows Count'].idxmax()]
 
print('\nYear with Highest Ever TV Shows Count are : All Platforms Combined\n')
df_year_high_tvshows.head(5)

Year with Highest Ever TV Shows Count are : All Platforms Combined

	Year	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	2017	605	214	116	276	19
1	2018	554	271	129	162	14
2	2016	543	212	115	226	10
3	2015	429	171	115	165	9
4	2019	382	231	94	44	21

fig = px.bar(y = df_year_high_tvshows['TV Shows Count'][:10],
             x = df_year_high_tvshows['Year'][:10], 
             color = df_year_high_tvshows['TV Shows Count'][:10],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows Count', 'x' : 'Year : In Minutes'},
             title  = 'Year with Highest TV Shows Count : All Platforms')

fig.update_layout(plot_bgcolor = "white")
fig.show()

# Lowest TV Shows Count per Year - All Platforms Combined
df_year_low_tvshows = year_data_tvshows.sort_values(by = 'TV Shows Count', ascending = True).reset_index()
df_year_low_tvshows = df_year_low_tvshows.drop(['index'], axis = 1)
# filter = (year_data_tvshows['TV Shows Count'] = =  (year_data_tvshows['TV Shows Count'].min()))
# df_year_low_tvshows = year_data_tvshows[filter]
 
print('\nYear with Lowest Ever TV Shows Count are : All Platforms Combined\n')
df_year_low_tvshows.head(5)

Year with Lowest Ever TV Shows Count are : All Platforms Combined

	Year	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	1901	1	0	0	1	0
1	1948	1	0	0	1	0
2	1949	1	0	0	1	0
3	1945	1	0	0	1	0
4	1944	1	0	0	1	0

fig = px.bar(y = df_year_low_tvshows['TV Shows Count'][:10],
             x = df_year_low_tvshows['Year'][:10], 
             color = df_year_low_tvshows['TV Shows Count'][:10],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows Count', 'x' : 'Year : In Minutes'},
             title  = 'Year with Lowest TV Shows Count : All Platforms')

fig.update_layout(plot_bgcolor = "white")
fig.show()

print(f'''
      Total '{df_tvshows_years['Year'].count()}' Titles are available on All Platforms, out of which\n
      You Can Choose to see TV Shows from Total '{year_data_tvshows['Year'].unique().shape[0]}' Year, They were Like this, \n
 
      {year_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)['Year'].head(5).unique()} etc. \n
 
      The Year with Highest TV Shows Count have '{year_data_tvshows['TV Shows Count'].max()}' TV Shows Available is '{df_year_high_tvshows['Year'][0]}', &\n
      The Year with Lowest TV Shows Count have '{year_data_tvshows['TV Shows Count'].min()}' TV Shows Available is '{df_year_low_tvshows['Year'][0]}'
      ''')

      Total '5432' Titles are available on All Platforms, out of which

      You Can Choose to see TV Shows from Total '89' Year, They were Like this, 

 
      [2017 2018 2016 2015 2019] etc. 

 
      The Year with Highest TV Shows Count have '605' TV Shows Available is '2017', &

      The Year with Lowest TV Shows Count have '1' TV Shows Available is '1901'
      

# Highest TV Shows Count per Year - Netflix
netflix_year_tvshows = year_data_tvshows[year_data_tvshows['Netflix'] !=  0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_year_tvshows = netflix_year_tvshows.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
 
netflix_year_high_tvshows = df_year_high_tvshows.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_year_high_tvshows = netflix_year_high_tvshows.drop(['index'], axis = 1)
 
netflix_year_low_tvshows = df_year_high_tvshows.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_year_low_tvshows = netflix_year_low_tvshows.drop(['index'], axis = 1)
 
netflix_year_high_tvshows.head(5)

	Year	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	2018	554	271	129	162	14
1	2019	382	231	94	44	21
2	2017	605	214	116	276	19
3	2016	543	212	115	226	10
4	2015	429	171	115	165	9

# Highest TV Shows Count per Year - Hulu
hulu_year_tvshows = year_data_tvshows[year_data_tvshows['Hulu'] !=  0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_year_tvshows = hulu_year_tvshows.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
 
hulu_year_high_tvshows = df_year_high_tvshows.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_year_high_tvshows = hulu_year_high_tvshows.drop(['index'], axis = 1)
 
hulu_year_low_tvshows = df_year_high_tvshows.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_year_low_tvshows = hulu_year_low_tvshows.drop(['index'], axis = 1)
 
hulu_year_high_tvshows.head(5)

	Year	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	2018	554	271	129	162	14
1	2017	605	214	116	276	19
2	2016	543	212	115	226	10
3	2015	429	171	115	165	9
4	2014	368	137	114	134	10

# Highest TV Shows Count per Year - Prime Video
prime_video_year_tvshows = year_data_tvshows[year_data_tvshows['Prime Video'] !=  0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_year_tvshows = prime_video_year_tvshows.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'TV Shows Count'], axis = 1)
 
prime_video_year_high_tvshows = df_year_high_tvshows.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_year_high_tvshows = prime_video_year_high_tvshows.drop(['index'], axis = 1)
 
prime_video_year_low_tvshows = df_year_high_tvshows.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_year_low_tvshows = prime_video_year_low_tvshows.drop(['index'], axis = 1)
 
prime_video_year_high_tvshows.head(5)

	Year	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	2017	605	214	116	276	19
1	2016	543	212	115	226	10
2	2015	429	171	115	165	9
3	2018	554	271	129	162	14
4	2014	368	137	114	134	10

# Highest TV Shows Count per Year - Disney+
disney_year_tvshows = year_data_tvshows[year_data_tvshows['Disney+'] !=  0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_year_tvshows = disney_year_tvshows.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'TV Shows Count'], axis = 1)
 
disney_year_high_tvshows = df_year_high_tvshows.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_year_high_tvshows = disney_year_high_tvshows.drop(['index'], axis = 1)
 
disney_year_low_tvshows = df_year_high_tvshows.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_year_low_tvshows = disney_year_low_tvshows.drop(['index'], axis = 1)
 
disney_year_high_tvshows.head(5)

	Year	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	2019	382	231	94	44	21
1	2017	605	214	116	276	19
2	2018	554	271	129	162	14
3	2012	283	79	96	117	11
4	2016	543	212	115	226	10

print(f'''
      The Year with Highest TV Shows Count Ever Got is '{df_year_high_tvshows['Year'][0]}' : '{df_year_high_tvshows['TV Shows Count'].max()}'\n
      The Year with Lowest TV Shows Count Ever Got is '{df_year_low_tvshows['Year'][0]}' : '{df_year_low_tvshows['TV Shows Count'].min()}'\n
      
      The Year with Highest TV Shows Count on 'Netflix' is '{netflix_year_high_tvshows['Year'][0]}' : '{netflix_year_high_tvshows['Netflix'].max()}'\n
      The Year with Lowest TV Shows Count on 'Netflix' is '{netflix_year_low_tvshows['Year'][0]}' : '{netflix_year_low_tvshows['Netflix'].min()}'\n
      
      The Year with Highest TV Shows Count on 'Hulu' is '{hulu_year_high_tvshows['Year'][0]}' : '{hulu_year_high_tvshows['Hulu'].max()}'\n
      The Year with Lowest TV Shows Count on 'Hulu' is '{hulu_year_low_tvshows['Year'][0]}' : '{hulu_year_low_tvshows['Hulu'].min()}'\n
      
      The Year with Highest TV Shows Count on 'Prime Video' is '{prime_video_year_high_tvshows['Year'][0]}' : '{prime_video_year_high_tvshows['Prime Video'].max()}'\n
      The Year with Lowest TV Shows Count on 'Prime Video' is '{prime_video_year_low_tvshows['Year'][0]}' : '{prime_video_year_low_tvshows['Prime Video'].min()}'\n
      
      The Year with Highest TV Shows Count on 'Disney+' is '{disney_year_high_tvshows['Year'][0]}' : '{disney_year_high_tvshows['Disney+'].max()}'\n
      The Year with Lowest TV Shows Count on 'Disney+' is '{disney_year_low_tvshows['Year'][0]}' : '{disney_year_low_tvshows['Disney+'].min()}'\n 
      ''')

      The Year with Highest TV Shows Count Ever Got is '2017' : '605'

      The Year with Lowest TV Shows Count Ever Got is '1901' : '1'

      
      The Year with Highest TV Shows Count on 'Netflix' is '2018' : '271'

      The Year with Lowest TV Shows Count on 'Netflix' is '1970' : '0'

      
      The Year with Highest TV Shows Count on 'Hulu' is '2018' : '129'

      The Year with Lowest TV Shows Count on 'Hulu' is '1901' : '0'

      
      The Year with Highest TV Shows Count on 'Prime Video' is '2017' : '276'

      The Year with Lowest TV Shows Count on 'Prime Video' is '1914' : '0'

      
      The Year with Highest TV Shows Count on 'Disney+' is '2019' : '21'

      The Year with Lowest TV Shows Count on 'Disney+' is '1970' : '0'
 
      

print(f'''
      Accross All Platforms the Average TV Shows Count of Year is '{round(year_data_tvshows['TV Shows Count'].mean(), ndigits = 2)}'\n
      The Average TV Shows Count of Year on 'Netflix' is '{round(netflix_year_tvshows['Netflix'].mean(), ndigits = 2)}'\n
      The Average TV Shows Count of Year on 'Hulu' is '{round(hulu_year_tvshows['Hulu'].mean(), ndigits = 2)}'\n
      The Average TV Shows Count of Year on 'Prime Video' is '{round(prime_video_year_tvshows['Prime Video'].mean(), ndigits = 2)}'\n
      The Average TV Shows Count of Year on 'Disney+' is '{round(disney_year_tvshows['Disney+'].mean(), ndigits = 2)}'\n 
      ''')

      Accross All Platforms the Average TV Shows Count of Year is '61.03'

      The Average TV Shows Count of Year on 'Netflix' is '37.84'

      The Average TV Shows Count of Year on 'Hulu' is '23.49'

      The Average TV Shows Count of Year on 'Prime Video' is '24.9'

      The Average TV Shows Count of Year on 'Disney+' is '4.46'
 
      

print(f'''
      Accross All Platforms Total Count of Year is '{year_data_tvshows['Year'].unique().shape[0]}'\n
      Total Count of Year on 'Netflix' is '{netflix_year_tvshows['Year'].unique().shape[0]}'\n
      Total Count of Year on 'Hulu' is '{hulu_year_tvshows['Year'].unique().shape[0]}'\n
      Total Count of Year on 'Prime Video' is '{prime_video_year_tvshows['Year'].unique().shape[0]}'\n
      Total Count of Year on 'Disney+' is '{disney_year_tvshows['Year'].unique().shape[0]}'\n 
      ''')

      Accross All Platforms Total Count of Year is '89'

      Total Count of Year on 'Netflix' is '49'

      Total Count of Year on 'Hulu' is '68'

      Total Count of Year on 'Prime Video' is '88'

      Total Count of Year on 'Disney+' is '41'
 
      

fig = plt.figure(figsize = (20, 10))
sns.lineplot(data = year_data_tvshows, x = 'Year', y = 'TV Shows Count')
plt.show()

plt.figure(figsize = (20, 10))
sns.lineplot(x = year_data_tvshows['Year'], y = year_data_tvshows['Netflix'], color = 'red')
sns.lineplot(x = year_data_tvshows['Year'], y = year_data_tvshows['Hulu'], color = 'lightgreen')
sns.lineplot(x = year_data_tvshows['Year'], y = year_data_tvshows['Prime Video'], color = 'lightblue')
sns.lineplot(x = year_data_tvshows['Year'], y = year_data_tvshows['Disney+'], color = 'darkblue')
plt.xlabel('Release Year', fontsize = 15)
plt.ylabel('TV Shows Count', fontsize = 15)
plt.show()

fig, axes = plt.subplots(2, 2,figsize=(20 ,20))
 
n_y_ax1 = sns.lineplot(x = year_data_tvshows['Year'], y = year_data_tvshows['Netflix'], color = 'red', ax = axes[0, 0])
h_y_ax2 = sns.lineplot(x = year_data_tvshows['Year'], y = year_data_tvshows['Hulu'], color = 'lightgreen', ax = axes[0, 1])
p_y_ax3 = sns.lineplot(x = year_data_tvshows['Year'], y = year_data_tvshows['Prime Video'], color = 'lightblue', ax = axes[1, 0])
d_y_ax4 = sns.lineplot(x = year_data_tvshows['Year'], y = year_data_tvshows['Disney+'], color = 'darkblue', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_y_ax1.title.set_text(labels[0])
h_y_ax2.title.set_text(labels[1])
p_y_ax3.title.set_text(labels[2])
d_y_ax4.title.set_text(labels[3])
 
plt.show()

def round_val(data):
    if str(data) != 'nan':
        return round(data)
        
def round_fix(data):
    if data in range(1801,1901):
        # print(data)
        return 1900
    if data in range(1901,1911):
        return 1910
    if data in range(1911,1921):
        return 1920
    if data in range(1921,1931):
        return 1930
    if data in range(1931,1941):
        return 1940
    if data in range(1941,1951):
        return 1950
    if data in range(1951,1961):
        return 1960
    if data in range(1961,1971):
        return 1970
    if data in range(1971,1981):
        return 1980
    if data in range(1981,1991):
        return 1990
    if data in range(1991,2001):
        return 2000
    if data in range(2000,2011):
        return 2010
    if data in range(2010,2021):
        return 2020
    if data in range(2020,2031):
        return 2030
    else:
        return 2100

df_tvshows_years_group['Year Group'] = df_tvshows_years_group['Year'].apply(round_fix).astype(int)
 
years_values = df_tvshows_years_group['Year Group'].value_counts().sort_index(ascending = False).tolist()
years_index = df_tvshows_years_group['Year Group'].value_counts().sort_index(ascending = False).index
 
# years_values, years_index

years_group_count = df_tvshows_years_group.groupby('Year Group')['Title'].count()
years_group_tvshows = df_tvshows_years_group.groupby('Year Group')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
years_group_data_tvshows = pd.concat([years_group_count, years_group_tvshows], axis = 1).reset_index().rename(columns = {'Title' : 'TV Shows Count'})
years_group_data_tvshows = years_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)

# Year Group with TV Shows Counts - All Platforms Combined
years_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)

	Year Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
11	2020	3864	1577	1006	1348	114
10	2010	985	213	384	487	33
9	2000	271	34	103	155	20
8	1990	126	19	48	70	11
7	1980	60	4	19	38	2
6	1970	56	4	24	34	2
5	1960	42	2	10	35	1
4	1950	16	0	3	13	0
3	1940	7	0	0	7	0
0	1910	2	0	0	2	0
2	1930	2	0	0	2	0
1	1920	1	1	0	0	0

years_group_data_tvshows.sort_values(by = 'Year Group', ascending = False)

	Year Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
11	2020	3864	1577	1006	1348	114
10	2010	985	213	384	487	33
9	2000	271	34	103	155	20
8	1990	126	19	48	70	11
7	1980	60	4	19	38	2
6	1970	56	4	24	34	2
5	1960	42	2	10	35	1
4	1950	16	0	3	13	0
3	1940	7	0	0	7	0
2	1930	2	0	0	2	0
1	1920	1	1	0	0	0
0	1910	2	0	0	2	0

fig = px.bar(y = years_group_data_tvshows['TV Shows Count'],
             x = years_group_data_tvshows['Year Group'], 
             color = years_group_data_tvshows['Year Group'],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows Count', 'x' : 'Year : In Minutes'},
             title  = 'TV Shows with Group Year in Minutes : All Platforms')

fig.update_layout(plot_bgcolor = "white")
fig.show()

fig = px.pie(years_group_data_tvshows[:10],
             names = years_group_data_tvshows['Year Group'],
             values = years_group_data_tvshows['TV Shows Count'],
             color = years_group_data_tvshows['TV Shows Count'],
             color_discrete_sequence = px.colors.sequential.Teal)

fig.update_traces(textinfo = 'percent+label',
                  title = 'TV Shows Count based on Year Group')
fig.show()

df_years_group_high_tvshows = years_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False).reset_index()
df_years_group_high_tvshows = df_years_group_high_tvshows.drop(['index'], axis = 1)
# filter = (years_group_data_tvshows['TV Shows Count'] ==  (years_group_data_tvshows['TV Shows Count'].max()))
# df_years_group_high_tvshows = years_group_data_tvshows[filter]
 
# highest_rated_tvshows = years_group_data_tvshows.loc[years_group_data_tvshows['TV Shows Count'].idxmax()]
 
# print('\nYear with Highest Ever TV Shows Count are : All Platforms Combined\n')
df_years_group_high_tvshows.head(5)

	Year Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	2020	3864	1577	1006	1348	114
1	2010	985	213	384	487	33
2	2000	271	34	103	155	20
3	1990	126	19	48	70	11
4	1980	60	4	19	38	2

df_years_group_low_tvshows = years_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = True).reset_index()
df_years_group_low_tvshows = df_years_group_low_tvshows.drop(['index'], axis = 1)
# filter = (years_group_data_tvshows['TV Shows Count'] = =  (years_group_data_tvshows['TV Shows Count'].min()))
# df_years_group_low_tvshows = years_group_data_tvshows[filter]
 
# print('\nYear with Lowest Ever TV Shows Count are : All Platforms Combined\n')
df_years_group_low_tvshows.head(5)

	Year Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	1920	1	1	0	0	0
1	1910	2	0	0	2	0
2	1930	2	0	0	2	0
3	1940	7	0	0	7	0
4	1950	16	0	3	13	0

print(f'''
      Total '{df_tvshows_years['Year'].count()}' Titles are available on All Platforms, out of which\n
      You Can Choose to see TV Shows from Total '{years_group_data_tvshows['Year Group'].unique().shape[0]}' Year Group, They were Like this, \n
 
      {years_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)['Year Group'].unique()} etc. \n
 
      The Year Group with Highest TV Shows Count have '{years_group_data_tvshows['TV Shows Count'].max()}' TV Shows Available is '{df_years_group_high_tvshows['Year Group'][0]}', &\n
      The Year Group with Lowest TV Shows Count have '{years_group_data_tvshows['TV Shows Count'].min()}' TV Shows Available is '{df_years_group_low_tvshows['Year Group'][0]}'
      ''')

      Total '5432' Titles are available on All Platforms, out of which

      You Can Choose to see TV Shows from Total '12' Year Group, They were Like this, 

 
      [2020 2010 2000 1990 1980 1970 1960 1950 1940 1910 1930 1920] etc. 

 
      The Year Group with Highest TV Shows Count have '3864' TV Shows Available is '2020', &

      The Year Group with Lowest TV Shows Count have '1' TV Shows Available is '1920'
      

netflix_years_group_tvshows = years_group_data_tvshows[years_group_data_tvshows['Netflix'] !=  0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_years_group_tvshows = netflix_years_group_tvshows.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
 
netflix_years_group_high_tvshows = df_years_group_high_tvshows.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_years_group_high_tvshows = netflix_years_group_high_tvshows.drop(['index'], axis = 1)
 
netflix_years_group_low_tvshows = df_years_group_high_tvshows.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_years_group_low_tvshows = netflix_years_group_low_tvshows.drop(['index'], axis = 1)
 
netflix_years_group_high_tvshows.head(5)

	Year Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	2020	3864	1577	1006	1348	114
1	2010	985	213	384	487	33
2	2000	271	34	103	155	20
3	1990	126	19	48	70	11
4	1980	60	4	19	38	2

hulu_years_group_tvshows = years_group_data_tvshows[years_group_data_tvshows['Hulu'] !=  0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_years_group_tvshows = hulu_years_group_tvshows.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
 
hulu_years_group_high_tvshows = df_years_group_high_tvshows.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_years_group_high_tvshows = hulu_years_group_high_tvshows.drop(['index'], axis = 1)
 
hulu_years_group_low_tvshows = df_years_group_high_tvshows.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_years_group_low_tvshows = hulu_years_group_low_tvshows.drop(['index'], axis = 1)
 
hulu_years_group_high_tvshows.head(5)

	Year Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	2020	3864	1577	1006	1348	114
1	2010	985	213	384	487	33
2	2000	271	34	103	155	20
3	1990	126	19	48	70	11
4	1970	56	4	24	34	2

prime_video_years_group_tvshows = years_group_data_tvshows[years_group_data_tvshows['Prime Video'] !=  0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_years_group_tvshows = prime_video_years_group_tvshows.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'TV Shows Count'], axis = 1)
 
prime_video_years_group_high_tvshows = df_years_group_high_tvshows.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_years_group_high_tvshows = prime_video_years_group_high_tvshows.drop(['index'], axis = 1)
 
prime_video_years_group_low_tvshows = df_years_group_high_tvshows.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_years_group_low_tvshows = prime_video_years_group_low_tvshows.drop(['index'], axis = 1)
 
prime_video_years_group_high_tvshows.head(5)

	Year Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	2020	3864	1577	1006	1348	114
1	2010	985	213	384	487	33
2	2000	271	34	103	155	20
3	1990	126	19	48	70	11
4	1980	60	4	19	38	2

disney_years_group_tvshows = years_group_data_tvshows[years_group_data_tvshows['Disney+'] !=  0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_years_group_tvshows = disney_years_group_tvshows.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'TV Shows Count'], axis = 1)
 
disney_years_group_high_tvshows = df_years_group_high_tvshows.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_years_group_high_tvshows = disney_years_group_high_tvshows.drop(['index'], axis = 1)
 
disney_years_group_low_tvshows = df_years_group_high_tvshows.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_years_group_low_tvshows = disney_years_group_low_tvshows.drop(['index'], axis = 1)
 
disney_years_group_high_tvshows.head(5)

	Year Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	2020	3864	1577	1006	1348	114
1	2010	985	213	384	487	33
2	2000	271	34	103	155	20
3	1990	126	19	48	70	11
4	1980	60	4	19	38	2

print(f'''
      The Year Group with Highest TV Shows Count Ever Got is '{df_years_group_high_tvshows['Year Group'][0]}' : '{df_years_group_high_tvshows['TV Shows Count'].max()}'\n
      The Year Group with Lowest TV Shows Count Ever Got is '{df_years_group_low_tvshows['Year Group'][0]}' : '{df_years_group_low_tvshows['TV Shows Count'].min()}'\n
      
      The Year Group with Highest TV Shows Count on 'Netflix' is '{netflix_years_group_high_tvshows['Year Group'][0]}' : '{netflix_years_group_high_tvshows['Netflix'].max()}'\n
      The Year Group with Lowest TV Shows Count on 'Netflix' is '{netflix_years_group_low_tvshows['Year Group'][0]}' : '{netflix_years_group_low_tvshows['Netflix'].min()}'\n
      
      The Year Group with Highest TV Shows Count on 'Hulu' is '{hulu_years_group_high_tvshows['Year Group'][0]}' : '{hulu_years_group_high_tvshows['Hulu'].max()}'\n
      The Year Group with Lowest TV Shows Count on 'Hulu' is '{hulu_years_group_low_tvshows['Year Group'][0]}' : '{hulu_years_group_low_tvshows['Hulu'].min()}'\n
      
      The Year Group with Highest TV Shows Count on 'Prime Video' is '{prime_video_years_group_high_tvshows['Year Group'][0]}' : '{prime_video_years_group_high_tvshows['Prime Video'].max()}'\n
      The Year Group with Lowest TV Shows Count on 'Prime Video' is '{prime_video_years_group_low_tvshows['Year Group'][0]}' : '{prime_video_years_group_low_tvshows['Prime Video'].min()}'\n
      
      The Year Group with Highest TV Shows Count on 'Disney+' is '{disney_years_group_high_tvshows['Year Group'][0]}' : '{disney_years_group_high_tvshows['Disney+'].max()}'\n
      The Year Group with Lowest TV Shows Count on 'Disney+' is '{disney_years_group_low_tvshows['Year Group'][0]}' : '{disney_years_group_low_tvshows['Disney+'].min()}'\n 
      ''')

      The Year Group with Highest TV Shows Count Ever Got is '2020' : '3864'

      The Year Group with Lowest TV Shows Count Ever Got is '1920' : '1'

      
      The Year Group with Highest TV Shows Count on 'Netflix' is '2020' : '1577'

      The Year Group with Lowest TV Shows Count on 'Netflix' is '1950' : '0'

      
      The Year Group with Highest TV Shows Count on 'Hulu' is '2020' : '1006'

      The Year Group with Lowest TV Shows Count on 'Hulu' is '1940' : '0'

      
      The Year Group with Highest TV Shows Count on 'Prime Video' is '2020' : '1348'

      The Year Group with Lowest TV Shows Count on 'Prime Video' is '1920' : '0'

      
      The Year Group with Highest TV Shows Count on 'Disney+' is '2020' : '114'

      The Year Group with Lowest TV Shows Count on 'Disney+' is '1950' : '0'
 
      

fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_ru_ax1 = sns.barplot(x = netflix_years_group_tvshows['Year Group'], y = netflix_years_group_tvshows['Netflix'], palette = 'Reds_r', ax = axes[0, 0])
h_ru_ax2 = sns.barplot(x = hulu_years_group_tvshows['Year Group'], y = hulu_years_group_tvshows['Hulu'], palette = 'Greens_r', ax = axes[0, 1])
p_ru_ax3 = sns.barplot(x = prime_video_years_group_tvshows['Year Group'], y = prime_video_years_group_tvshows['Prime Video'], palette = 'Blues_r', ax = axes[1, 0])
d_ru_ax4 = sns.barplot(x = disney_years_group_tvshows['Year Group'], y = disney_years_group_tvshows['Disney+'], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_ru_ax1.title.set_text(labels[0])
h_ru_ax2.title.set_text(labels[1])
p_ru_ax3.title.set_text(labels[2])
d_ru_ax4.title.set_text(labels[3])
 
plt.show()

plt.figure(figsize = (20, 5))
sns.lineplot(x = years_group_data_tvshows['Year Group'], y = years_group_data_tvshows['Netflix'], color = 'red')
sns.lineplot(x = years_group_data_tvshows['Year Group'], y = years_group_data_tvshows['Hulu'], color = 'lightgreen')
sns.lineplot(x = years_group_data_tvshows['Year Group'], y = years_group_data_tvshows['Prime Video'], color = 'lightblue')
sns.lineplot(x = years_group_data_tvshows['Year Group'], y = years_group_data_tvshows['Disney+'], color = 'darkblue')
plt.xlabel('Year Group', fontsize = 15)
plt.ylabel('TV Shows Count', fontsize = 15)
plt.show()

print(f'''
      Accross All Platforms Total Count of Year Group is '{years_group_data_tvshows['Year Group'].unique().shape[0]}'\n
      Total Count of Year Group on 'Netflix' is '{netflix_years_group_tvshows['Year Group'].unique().shape[0]}'\n
      Total Count of Year Group on 'Hulu' is '{hulu_years_group_tvshows['Year Group'].unique().shape[0]}'\n
      Total Count of Year Group on 'Prime Video' is '{prime_video_years_group_tvshows['Year Group'].unique().shape[0]}'\n
      Total Count of Year Group on 'Disney+' is '{disney_years_group_tvshows['Year Group'].unique().shape[0]}'\n 
      ''')

      Accross All Platforms Total Count of Year Group is '12'

      Total Count of Year Group on 'Netflix' is '8'

      Total Count of Year Group on 'Hulu' is '8'

      Total Count of Year Group on 'Prime Video' is '11'

      Total Count of Year Group on 'Disney+' is '7'
 
      

fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_ru_ax1 = sns.lineplot(y = years_group_data_tvshows['Year Group'], x = years_group_data_tvshows['Netflix'], color = 'red', ax = axes[0, 0])
h_ru_ax2 = sns.lineplot(y = years_group_data_tvshows['Year Group'], x = years_group_data_tvshows['Hulu'], color = 'lightgreen', ax = axes[0, 1])
p_ru_ax3 = sns.lineplot(y = years_group_data_tvshows['Year Group'], x = years_group_data_tvshows['Prime Video'], color = 'lightblue', ax = axes[1, 0])
d_ru_ax4 = sns.lineplot(y = years_group_data_tvshows['Year Group'], x = years_group_data_tvshows['Disney+'], color = 'darkblue', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_ru_ax1.title.set_text(labels[0])
h_ru_ax2.title.set_text(labels[1])
p_ru_ax3.title.set_text(labels[2])
d_ru_ax4.title.set_text(labels[3])

plt.show()

fig, axes = plt.subplots(2, 2,figsize=(20 ,20))
 
n_yg_ax1 = sns.lineplot(x = years_group_data_tvshows['Year Group'], y = years_group_data_tvshows['Netflix'], color = 'red', ax = axes[0, 0])
h_yg_ax2 = sns.lineplot(x = years_group_data_tvshows['Year Group'], y = years_group_data_tvshows['Hulu'], color = 'lightgreen', ax = axes[0, 1])
p_yg_ax3 = sns.lineplot(x = years_group_data_tvshows['Year Group'], y = years_group_data_tvshows['Prime Video'], color = 'lightblue', ax = axes[1, 0])
d_yg_ax4 = sns.lineplot(x = years_group_data_tvshows['Year Group'], y = years_group_data_tvshows['Disney+'], color = 'darkblue', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_yg_ax1.title.set_text(labels[0])
h_yg_ax2.title.set_text(labels[1])
p_yg_ax3.title.set_text(labels[2])
d_yg_ax4.title.set_text(labels[3])
 
plt.show()

fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_ru_ax1 = sns.barplot(x = years_group_data_tvshows['Year Group'], y = years_group_data_tvshows['Netflix'], palette = 'Reds_r', ax = axes[0, 0])
h_ru_ax2 = sns.barplot(x = years_group_data_tvshows['Year Group'], y = years_group_data_tvshows['Hulu'], palette = 'Greens_r', ax = axes[0, 1])
p_ru_ax3 = sns.barplot(x = years_group_data_tvshows['Year Group'], y = years_group_data_tvshows['Prime Video'], palette = 'Blues_r', ax = axes[1, 0])
d_ru_ax4 = sns.barplot(x = years_group_data_tvshows['Year Group'], y = years_group_data_tvshows['Disney+'], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_ru_ax1.title.set_text(labels[0])
h_ru_ax2.title.set_text(labels[1])
p_ru_ax3.title.set_text(labels[2])
d_ru_ax4.title.set_text(labels[3])

plt.show()